While there have been many visualization approaches targeted towards large datansets, such as active learning [1]}, [2]}, [3]}, interactive labeling [4]}, [5]}, labeling process improvements [6]}, [7]}, and data cleaning [8]}, [9]}, they are all used during the labeling process.
Fairness analysis, on the contrary, happens either after dataset collection has been completed (data fairness), or even after model training has been finished (model fairness).
Thus, in the following, our focus is on visualization techniques for bias mitigation.
